• Therapeutic Science for Rehabilitation
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• v.1 no.2
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• pp.41-53
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• 2012

Objective : The purpose of the research is to investigate Korean occupational therapists' awareness of Evidence-Based Practice(EBP), basis of clinical decision making, barrier factors of EBP execution. Methods : Form December 2009 to March 2010, 500 questionnaires were sent out to clinical occupational therapists and 160 questionnaire which are returned by the therapists were analyzed through descriptive statistics. Results : There are more female respondents than male. More than 90% of them had less than 5 years experience. Most of respondents hadn't experienced education concerning EBP(76%), and the education is not periodically conducted(55%). Moreover, treatment was being tailored based on subjective judgement. On the other hand, the degree of recognition showed that the professionalism of occupational therapists can be improved through EBP(96%), and be helpful to make clinical decision(88%). Most of the therapists answered that guaranteeing enough time to search the basis of treatment(90%) and participating in the education course(92%) is needed to make EBP applicable. This result shows that while the application rate of EBP is low, the recognition rate of EBP is high, which means it is necessary to revitalize EBP education program. Conclusions : In order to provide quality service, education course and instructor training program is needed. We should also make a great effort to offer evidence-based education to occupational therapy students. Furthermore, EBP has to be carried out to improve the professionalism of occupational therapists, and the research about education program and its effect has to be executed.

• Journal of Digital Contents Society
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• v.17 no.2
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• pp.71-79
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• 2016

This study is intended to compare the performances of sound source localization methods used for stable automobile control by improving voice recognition rate in automobile environment and suggest how to improve their performances. Generally, sound source location estimation methods employ the TDOA algorithm, and there are two ways for it; one is to use a cross correlation function in the time domain, and the other is GCC-PHAT calculated in the frequency domain. Among these ways, GCC-PHAT is known to have stronger characteristics against echo and noise than the cross correlation function. This study compared the performances of the two methods above in automobile environment full of echo and vibration noise and suggested the use of a median filter additionally. We found that median filter helps both estimation methods have good performances and variance values to be decreased. According to the experimental results, there is almost no difference in the two methods' performances in the experiment using voice; however, using the signal of a song, GCC-PHAT is 10% more excellent than the cross correlation function in terms of the recognition rate. Also, when the median filter was added, the cross correlation function's recognition rate could be improved up to 11%. And in regarding to variance values, both methods showed stable performances.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.1-9
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• 2020

As pet dogs rapidly increase in number, abandoned and lost dogs are also increasing in number. In Korea, animal registration has been in force since 2014, but the registration rate is not high owing to safety and effectiveness issues. Biometrics is attracting attention as an alternative. In order to increase the recognition rate from biometrics, it is necessary to collect biometric images in the same form as much as possible-from the face. This paper proposes a method to determine whether a dog is facing front or not in a real-time video. The proposed method detects the dog's eyes and nose using deep learning, and extracts five types of directional face information through the relative size and position of the detected face. Then, a machine learning classifier determines whether the dog is facing front or not. We used 2,000 dog images for learning, verification, and testing. YOLOv3 and YOLOv4 were used to detect the eyes and nose, and Multi-layer Perceptron (MLP), Random Forest (RF), and the Support Vector Machine (SVM) were used as classifiers. When YOLOv4 and the RF classifier were used with all five types of the proposed face orientation information, the face recognition rate was best, at 95.25%, and we found that real-time processing is possible.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.351-361
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• 2021

This study was conducted to identify the factors of hypertension treatment in Gwangju and to establish a hypertension cure rate management plan by using local community health surveys to provide the hypertension cure rate management plan centering around the wellness local community. The research collected 13,714 Gwangju research data among a total of 685,820 local community health surveys of KDCA (Korea Disease Control and Prevention Agency) from 2017 to 2019. Among the data, 2,941 subjects, those with diagnosed hypertension aged over 30, were selected and analyzed through SAS 9.4, SAS Enterprise Miner 15.1. The results are as follows. The differences in hypertension diagnosis cure rate in Gwangju based on the subjects' socioeconomic characteristics were shown in gender, age, marital status, level of educational attainment, economic activity status, and monthly income. The significant differences in hypertension cure rate based on health behavior characteristics were shown in current smoking, monthly alcohol consumption, high-risk drinking, breakfast, recognition of good health level, diabetes and treatment, annual unmet medical needs, and annual health center use. As a result of the logistic regression analysis and interactive decision tree analysis to identify the factors affecting hypertension treatment, the research found that the factors that appear are age, marital status, diabetes and treatment, and annual unmet medical needs. Accordingly, to increase the recognition of the importance of hypertension treatment to people of young ages and not to develop complications, public health-educational effort in Gwangju is needed with an effective preparation plan.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.333-338
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• 2012

Human resource management is bringing the various changes with the IT technology. In particular, if HRM is non-scientific method such as group management, physical plant, working hours constraints, personal contacts, etc, the current enterprise human resources management(e-HRM) appeared in the individual dimension management, virtual workspace (for example: smart work center, home work, etc.), working time flexibility and elasticity, computer-based statistical data and the scientific method of analysis and management has been a big difference in the sense. Therefore, depending on changes in the environment, companies have introduced a variety of techniques as RFID card, fingerprint time & attendance systems in order to build more efficient and strategic human resource management system. In this paper, time and attendance, access control management system was developed using multi camera for 2D and 3D face recognition technology-based for efficient enterprise human resource management. We had an issue with existing 2D-style face-recognition technology for lighting and the attitude, and got more than 90% recognition rate against the poor readability. In addition, 3D face recognition has computational complexities, so we could improve hybrid video recognition and the speed using 3D and 2D in parallel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1573-1582
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• 2011

Risk of lung cancer among lung-related diseases has gradually increased during last decades. The chest digital radiography is the primary diagnosis method for lung cancer. Diagnosing lung cancer using this method requires doctors of ripe experience. Despite their experience there are often wrong diagnoses, which decrease early diagnosis and survival rates of patients. The aim of this study was intended to establish the base on the Computer Aided Diagnosis (CAD) by analyzing Image Recognition Algorithm using Principle component Analysis (PCA) and diagnosing patient's chest X-ray image. The database obtained through this approach enables a doctor to significantly reduce misdiagnosis during the early diagnosis stage, if he or she utilizes it as the preliminary reading step. Case studies were carried out using normal organ, and organs suffering from bronchogenic carcinoma and granuloma. A normal image and unique disease images were extracted after PCA analysis, and their cross-recognition efficiency were compared each other. The result revealed that the recognition rate was much high between normal and disease images, but relatively low between two disease images. In order to increase the recognition efficiency among chest diseases the related algorithms have to be developed continuously in the future study, and such effort will establish the resolute base for CAD.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.97-107
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• 2002

In this paper, we propose the realization of on-line handwritten Chinese character recognition for mobile personal digital assistants (PDA). We focus on the development of an algorithm having a high recognition performance under the restriction that PDA requires small memory storage and less computational complexity in comparison with PC. Therefore, we use index matching method having computational advantage for fast recognition and we suggest a unit reconstruction method to minimize the memory size to store the character models and to accomodate the various changes in stroke order and stroke number of each person in handwriting Chinese characters. We set up standard model consisting of 1800 characters using a set of pre-defined units. Input data are measured by similarity among candidate characters selected on the basis of stroke numbers and region features after preprocessing and feature extracting. We consider 1800 Chinese characters adopted in the middle and high school in Korea. We take character sets of five person, written in printed style, irrespective of stroke ordering and stroke numbers. As experimental results, we obtained an average recognition time of 0.16 second per character and the successful recognition rate of 94.3% with MIPS R4000 CPU in PDA.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.111-120
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• 2016

Wire recognition is one of the important tasks in chip reverse engineering since connectivity comes from wires. Recognized wires are used to recover logical or functional representation of the corresponding circuit. Though manual recognition provides accurate results, it becomes impossible, as the number of wires is more than hundreds of thousands. Wires on a chip usually have specific intensity or color characteristics since they are made of specific materials. This paper proposes two stage wire recognition scheme; image binarization and then the process of determining whether regions in binary image are wires or not. We employ existing techniques for two processes. Since the second process requires the characteristics of wires, the users needs to select the typical wire region in the given image. The histogram characteristic of the selected region is used in calculating histogram similarity between the typical wire region and the other regions. The first experiment is to select the most appropriate binarization scheme for the second process. The second experiment on the second process compares three proposed methods employing histogram similarity of grayscale or HSV color since there have not been proposed any wire recognition method comparable by experiment. The best method shows more than 98% of true positive rate for 25 test examples.

• Korean Journal of Cognitive Science
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• v.7 no.1
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• pp.57-74
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• 1996

This paper presents a comparison of recognition performance of the features used inthe recent handwritten korean character recognition.This research aims at providing the basis for feature selecion in order to improve not only the recognition rate but also the efficiency of recognition system.For the comparison of feature performace,we analyzed the characteristics of theose features and then,classified them into three rypes:global feature(image transformation)type,statistical feature type,and local/ topological feature type.For each type,we selected four or five features which seem more suitable to represent the characteristics of korean alphabet,and performed recongition experiments for the first consonant,horizontal vowel,and vertical vowel of a korean character, respectively.The classifier used in our experiments is a multi-layered perceptron with one hidden layer which is trained with backpropagation algorithm.The training and test data in the experiment are taken from 30sets of PE92. Experimental results show that 1)local/topological features outperform the other two type features in terms of recognition rates 2)mesh and projection features in statical feature type,walsh and DCT features in global feature type,and gradient and concavity features in local/topological feature type outperform the others in each type, respectively.